{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkzOKBirz271"
      },
      "source": [
        "# Anomaly detection with embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFWzQEqNosrS"
      },
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Anomaly_detection_with_embeddings.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQPvHyHCz7mk"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to use the embeddings from the Gemini API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) and detect outliers outside a particular radius of the central point of each categorical cluster.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LyLLYVEhzud8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/137.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
            "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m133.1/137.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.7/137.7 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -U -q \"google-genai>=1.0.0\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yi0kitgd5aLG"
      },
      "source": [
        "To run the following cell, your API key must be stored it in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see the [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) quickstart for an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6OeEZ5Bj5Zr8"
      },
      "outputs": [],
      "source": [
        "# Used to securely store your API key\n",
        "from google.colab import userdata\n",
        "from google import genai\n",
        "API_KEY = userdata.get(\"GOOGLE_API_KEY\")\n",
        "client = genai.Client(api_key=API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhWtEhZ6BO58"
      },
      "source": [
        "## Prepare dataset\n",
        "\n",
        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/stable/datasets/real_world.html#newsgroups-dataset) from the open-source [SciKit project](https://scikit-learn.org/) contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. This tutorial uses the training subset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YtHABp9BBTIt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['alt.atheism',\n",
              " 'comp.graphics',\n",
              " 'comp.os.ms-windows.misc',\n",
              " 'comp.sys.ibm.pc.hardware',\n",
              " 'comp.sys.mac.hardware',\n",
              " 'comp.windows.x',\n",
              " 'misc.forsale',\n",
              " 'rec.autos',\n",
              " 'rec.motorcycles',\n",
              " 'rec.sport.baseball',\n",
              " 'rec.sport.hockey',\n",
              " 'sci.crypt',\n",
              " 'sci.electronics',\n",
              " 'sci.med',\n",
              " 'sci.space',\n",
              " 'soc.religion.christian',\n",
              " 'talk.politics.guns',\n",
              " 'talk.politics.mideast',\n",
              " 'talk.politics.misc',\n",
              " 'talk.religion.misc']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "\n",
        "newsgroups_train = fetch_20newsgroups(subset=\"train\")\n",
        "\n",
        "# View list of class names for dataset\n",
        "newsgroups_train.target_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPKgmQDQC3zd"
      },
      "source": [
        "Here is the first example in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "CSXYP0JwBXHh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lines: 15\n",
            "\n",
            " I was wondering if anyone out there could enlighten me on this car I saw\n",
            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
            "the front bumper was separate from the rest of the body. This is \n",
            "all I know. If anyone can tellme a model name, engine specs, years\n",
            "of production, where this car is made, history, or whatever info you\n",
            "have on this funky looking car, please e-mail.\n",
            "\n",
            "Thanks,\n",
            "- IL\n",
            "   ---- brought to you by your neighborhood Lerxst ----\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "idx = newsgroups_train.data[0].index(\"Lines\")\n",
        "print(newsgroups_train.data[0][idx:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Raafa2naC6Ec"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
        "newsgroups_train.data = [\n",
        "    re.sub(r\"[\\w\\.-]+@[\\w\\.-]+\", \"\", d) for d in newsgroups_train.data\n",
        "]  # Remove email\n",
        "newsgroups_train.data = [\n",
        "    re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data\n",
        "]  # Remove names\n",
        "newsgroups_train.data = [\n",
        "    d.replace(\"From: \", \"\") for d in newsgroups_train.data\n",
        "]  # Remove \"From: \"\n",
        "newsgroups_train.data = [\n",
        "    d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data\n",
        "]  # Remove \"\\nSubject: \"\n",
        "\n",
        "# Cut off each text entry after 5,000 characters\n",
        "newsgroups_train.data = [\n",
        "    d[0:5000] if len(d) > 5000 else d for d in newsgroups_train.data\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ZjE_Lsr6IhEd"
      },
      "outputs": [
        {
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              "      <td>WHAT car is this!?\\nNntp-Posting-Host: rac3.w...</td>\n",
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            "text/plain": [
              "                                                    Text  Label  \\\n",
              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
              "...                                                  ...    ...   \n",
              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
              "\n",
              "                     Class Name  \n",
              "0                     rec.autos  \n",
              "1         comp.sys.mac.hardware  \n",
              "2         comp.sys.mac.hardware  \n",
              "3                 comp.graphics  \n",
              "4                     sci.space  \n",
              "...                         ...  \n",
              "11309                   sci.med  \n",
              "11310     comp.sys.mac.hardware  \n",
              "11311  comp.sys.ibm.pc.hardware  \n",
              "11312             comp.graphics  \n",
              "11313           rec.motorcycles  \n",
              "\n",
              "[11314 rows x 3 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Put training points into a dataframe\n",
        "df_train = pd.DataFrame(newsgroups_train.data, columns=[\"Text\"])\n",
        "df_train[\"Label\"] = newsgroups_train.target\n",
        "# Match label to target name index\n",
        "df_train[\"Class Name\"] = df_train[\"Label\"].map(\n",
        "    newsgroups_train.target_names.__getitem__\n",
        ")\n",
        "\n",
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7OHvTBaImpB"
      },
      "source": [
        "Next, sample some of the data by taking 150 data points in the training dataset and choosing a few categories. This tutorial uses the science categories."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "yPxwl05BIjWX"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-7-dc22d2141534>:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  .apply(lambda x: x.sample(SAMPLE_SIZE))\n"
          ]
        },
        {
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_train');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     index                                               Text  Label  \\\n",
              "0     1650   Re: White House Public Encryption Management ...     11   \n",
              "1     1651   Re: The [secret] source of that announcement\\...     11   \n",
              "2     1652   Re: text of White House announcement and Q&As...     11   \n",
              "3     1653   Re: White House Wiretap Chip Disinformation S...     11   \n",
              "4     1654  Tony Lezard <>Re: text of White House announce...     11   \n",
              "..     ...                                                ...    ...   \n",
              "595   2245  Leigh Palmer <>Re: Orion drive in vacuum -- ho...     14   \n",
              "596   2246   Re: A WRENCH in the works?\\nOrganization: Ken...     14   \n",
              "597   2247   Re: space news from Feb 15 AW&ST\\nOrganizatio...     14   \n",
              "598   2248  Subject: DC-X/Y/1 question\\n \\nKeywords: DC-X\\...     14   \n",
              "599   2249  MUNIZB% Long Island \\nX-Added: Forwarded by Sp...     14   \n",
              "\n",
              "    Class Name  \n",
              "0    sci.crypt  \n",
              "1    sci.crypt  \n",
              "2    sci.crypt  \n",
              "3    sci.crypt  \n",
              "4    sci.crypt  \n",
              "..         ...  \n",
              "595  sci.space  \n",
              "596  sci.space  \n",
              "597  sci.space  \n",
              "598  sci.space  \n",
              "599  sci.space  \n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Take a sample of each label category from df_train\n",
        "SAMPLE_SIZE = 150\n",
        "df_train = (\n",
        "    df_train.groupby(\"Label\", as_index=False)\n",
        "    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
        "    .reset_index(drop=True)\n",
        ")\n",
        "\n",
        "# Choose categories about science\n",
        "df_train = df_train[df_train[\"Class Name\"].str.contains(\"sci\")]\n",
        "\n",
        "# Reset the index\n",
        "df_train = df_train.reset_index()\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "UjTrEnmdIo5P"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class Name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sci.crypt</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.electronics</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.med</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.space</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "Class Name\n",
              "sci.crypt          150\n",
              "sci.electronics    150\n",
              "sci.med            150\n",
              "sci.space          150\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train[\"Class Name\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUgv8SOwXfAX"
      },
      "source": [
        "## Create the embeddings\n",
        "\n",
        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01I9uzbo4t26"
      },
      "source": [
        "### API changes to Embeddings with model embedding-001\n",
        "\n",
        "For the embeddings model, `text-embedding-004`, there is a task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
        "\n",
        "These parameters apply only to the embeddings models. The task types are:\n",
        "\n",
        "Task Type | Description\n",
        "---       | ---\n",
        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jkS_EWfAXcxc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d115f3568c0e4b77ba4408f39374504a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/6 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "from google.genai import types\n",
        "\n",
        "tqdm.pandas()\n",
        "\n",
        "from google.api_core import retry\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "\n",
        "def make_embed_text_fn(model):\n",
        "\n",
        "    @retry.Retry(timeout=300.0)\n",
        "    def embed_fn(texts: list[str]) -> list[list[float]]:\n",
        "        # Set the task_type to CLUSTERING and embed the batch of texts\n",
        "        embeddings = client.models.embed_content(\n",
        "            model=model,\n",
        "            contents=texts,\n",
        "            config=types.EmbedContentConfig(task_type=\"CLUSTERING\"),\n",
        "        ).embeddings\n",
        "        return np.array([embedding.values for embedding in embeddings])\n",
        "\n",
        "    return embed_fn\n",
        "\n",
        "\n",
        "def create_embeddings(df):\n",
        "    MODEL_ID = \"text-embedding-004\" # @param [\"embedding-001\",\"text-embedding-004\"] {allow-input: true}\n",
        "    model = f\"models/{MODEL_ID}\"\n",
        "    embed_fn = make_embed_text_fn(model)\n",
        "\n",
        "    batch_size = 100  # at most 100 requests can be in one batch\n",
        "    all_embeddings = []\n",
        "\n",
        "    # Loop over the texts in chunks of batch_size\n",
        "    for i in tqdm(range(0, len(df), batch_size)):\n",
        "        batch = df[\"Text\"].iloc[i : i + batch_size].tolist()\n",
        "        embeddings = embed_fn(batch)\n",
        "        all_embeddings.extend(embeddings)\n",
        "\n",
        "    df[\"Embeddings\"] = all_embeddings\n",
        "    return df\n",
        "\n",
        "\n",
        "df_train = create_embeddings(df_train)\n",
        "df_train.drop(\"index\", axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNTjKcD_aluG"
      },
      "source": [
        "## Dimensionality reduction\n",
        "\n",
        "The dimension of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BJDHDQmeZqy2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_train[\"Embeddings\"][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "S5-XU-twaoK6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(600, 768)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
        "X = np.array(df_train[\"Embeddings\"].to_list(), dtype=np.float32)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV-Y7iEtbAkm"
      },
      "source": [
        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "FhYKF-lObC04"
      },
      "outputs": [],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "tsne = TSNE(random_state=0, max_iter=1000)\n",
        "tsne_results = tsne.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "31wdqnp_bH9B"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 600,\n        \"samples\": [\n          -18.317607879638672,\n          21.307199478149414,\n          21.615800857543945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 600,\n        \"samples\": [\n          5.472441673278809,\n          17.822649002075195,\n          -13.283952713012695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_tsne"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-ba4f8fc8-abe3-4d0f-8e65-59d3b815addd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-21.515207</td>\n",
              "      <td>4.553598</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-15.827778</td>\n",
              "      <td>-8.150129</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-24.811195</td>\n",
              "      <td>5.841414</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-23.533512</td>\n",
              "      <td>6.998471</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-23.327581</td>\n",
              "      <td>2.978018</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>13.261472</td>\n",
              "      <td>-28.758375</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>4.294526</td>\n",
              "      <td>-32.239464</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>27.036417</td>\n",
              "      <td>-18.934540</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>4.773326</td>\n",
              "      <td>-21.166903</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>9.662366</td>\n",
              "      <td>-18.331491</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba4f8fc8-abe3-4d0f-8e65-59d3b815addd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ba4f8fc8-abe3-4d0f-8e65-59d3b815addd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ba4f8fc8-abe3-4d0f-8e65-59d3b815addd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-54689531-0e87-4f05-82ac-5999635c32f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54689531-0e87-4f05-82ac-5999635c32f0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-54689531-0e87-4f05-82ac-5999635c32f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_40a2598f-bc3a-4d35-b6fa-dd586c380de6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_40a2598f-bc3a-4d35-b6fa-dd586c380de6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name\n",
              "0   -21.515207   4.553598  sci.crypt\n",
              "1   -15.827778  -8.150129  sci.crypt\n",
              "2   -24.811195   5.841414  sci.crypt\n",
              "3   -23.533512   6.998471  sci.crypt\n",
              "4   -23.327581   2.978018  sci.crypt\n",
              "..         ...        ...        ...\n",
              "595  13.261472 -28.758375  sci.space\n",
              "596   4.294526 -32.239464  sci.space\n",
              "597  27.036417 -18.934540  sci.space\n",
              "598   4.773326 -21.166903  sci.space\n",
              "599   9.662366 -18.331491  sci.space\n",
              "\n",
              "[600 rows x 3 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_tsne = pd.DataFrame(tsne_results, columns=[\"TSNE1\", \"TSNE2\"])\n",
        "df_tsne[\"Class Name\"] = df_train[\n",
        "    \"Class Name\"\n",
        "]  # Add labels column from df_train to df_tsne\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pTj8HfhpbJ9X"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))  # Set figsize\n",
        "sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x=\"TSNE1\", y=\"TSNE2\", hue=\"Class Name\", palette=\"Set2\")\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title(\"Scatter plot of news using t-SNE\")\n",
        "plt.xlabel(\"TSNE1\")\n",
        "plt.ylabel(\"TSNE2\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JQbX4pcMdBe"
      },
      "source": [
        "## Outlier detection\n",
        "\n",
        "To determine which points are anomalous, you will determine which points are inliers and outliers. Start by finding the centroid, or location that represents the center of the cluster, and use the distance to determine the points that are outliers.\n",
        "\n",
        "Start by getting the centroid of each category."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nUIkLxtMK4qC"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"centroids\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -11.396698951721191,\n          16.557491302490234,\n          -19.444419860839844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15.218337059020996,\n          -20.563791275024414,\n          -3.684580087661743\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "centroids"
            },
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sci.crypt</th>\n",
              "      <td>-19.444420</td>\n",
              "      <td>-3.684580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.electronics</th>\n",
              "      <td>-11.396699</td>\n",
              "      <td>15.218337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.med</th>\n",
              "      <td>15.847226</td>\n",
              "      <td>5.903560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.space</th>\n",
              "      <td>16.557491</td>\n",
              "      <td>-20.563791</td>\n",
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              "        const element = document.querySelector('#df-14f0ca24-0e99-467d-b365-1597ff2d45de');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37298a44-9481-4e8b-91e0-8114f1f63e18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9eb26902-b678-491e-822f-604ea6d9b17d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('centroids')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9eb26902-b678-491e-822f-604ea6d9b17d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('centroids');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     TSNE1      TSNE2\n",
              "Class Name                           \n",
              "sci.crypt       -19.444420  -3.684580\n",
              "sci.electronics -11.396699  15.218337\n",
              "sci.med          15.847226   5.903560\n",
              "sci.space        16.557491 -20.563791"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_centroids(df_tsne):\n",
        "    # Get the centroid of each cluster\n",
        "    centroids = df_tsne.groupby(\"Class Name\").mean()\n",
        "    return centroids\n",
        "\n",
        "\n",
        "centroids = get_centroids(df_tsne)\n",
        "centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "GJH4Oo6E-r_6"
      },
      "outputs": [],
      "source": [
        "def get_embedding_centroids(df):\n",
        "    emb_centroids = dict()\n",
        "    grouped = df.groupby(\"Class Name\")\n",
        "    for c in grouped.groups:\n",
        "        sub_df = grouped.get_group(c)\n",
        "        # Get the centroid value of dimension 768\n",
        "        emb_centroids[c] = np.mean(sub_df[\"Embeddings\"], axis=0)\n",
        "\n",
        "    return emb_centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "1tas9Yg4_iyq"
      },
      "outputs": [],
      "source": [
        "emb_c = get_embedding_centroids(df_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMvdYLjKl32a"
      },
      "source": [
        "Plot each centroid you have found against the rest of the points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "jpN02WY3Ogji"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the centroids against the cluster\n",
        "fig, ax = plt.subplots(figsize=(8, 6))  # Set figsize\n",
        "sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x=\"TSNE1\", y=\"TSNE2\", hue=\"Class Name\", palette=\"Set2\")\n",
        "sns.scatterplot(\n",
        "    data=centroids,\n",
        "    x=\"TSNE1\",\n",
        "    y=\"TSNE2\",\n",
        "    color=\"black\",\n",
        "    marker=\"X\",\n",
        "    s=100,\n",
        "    label=\"Centroids\",\n",
        ")\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title(\"Scatter plot of news using t-SNE with centroids\")\n",
        "plt.xlabel(\"TSNE1\")\n",
        "plt.ylabel(\"TSNE2\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onFfUf1XoEQW"
      },
      "source": [
        "Choose a radius. Anything beyond this bound from the centroid of that category is considered an outlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "87cDfNpvOu7f"
      },
      "outputs": [],
      "source": [
        "def calculate_euclidean_distance(p1, p2):\n",
        "    return np.sqrt(np.sum(np.square(p1 - p2)))\n",
        "\n",
        "\n",
        "def detect_outlier(df, emb_centroids, radius):\n",
        "    for idx, row in df.iterrows():\n",
        "        class_name = row[\"Class Name\"]  # Get class name of row\n",
        "        # Compare centroid distances\n",
        "        dist = calculate_euclidean_distance(\n",
        "            row[\"Embeddings\"], emb_centroids[class_name]\n",
        "        )\n",
        "        df.at[idx, \"Outlier\"] = dist > radius\n",
        "\n",
        "    return len(df[df[\"Outlier\"] == True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "CsVsod5MKd3X"
      },
      "outputs": [],
      "source": [
        "range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()\n",
        "num_outliers = []\n",
        "for i in range_:\n",
        "    num_outliers.append(detect_outlier(df_train, emb_c, i))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "vReUSOjbNHQv"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot range_ and num_outliers\n",
        "fig = plt.figure(figsize=(14, 8))\n",
        "plt.rcParams.update({\"font.size\": 12})\n",
        "plt.bar(list(map(str, range_)), num_outliers)\n",
        "plt.title(\"Number of outliers vs. distance of points from centroid\")\n",
        "plt.xlabel(\"Distance\")\n",
        "plt.ylabel(\"Number of outliers\")\n",
        "for i in range(len(range_)):\n",
        "    plt.text(i, num_outliers[i], num_outliers[i], ha=\"center\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNISxrzwGvBH"
      },
      "source": [
        "Depending on how sensitive you want your anomaly detector to be, you can choose which radius you would like to use. For now, 0.62 is used, but you can change this value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "PMNFFSDOTELn"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_outliers\",\n  \"rows\": 498,\n  \"fields\": [\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 498,\n        \"samples\": [\n          \" Space FAQ 13/15 - Interest Groups & Publications\\nKeywords: Frequently Asked Questions\\nArticle-I.D.: cs.groups_733694492\\nExpires: 6 May 1993 20:01:32 GMT\\nDistribution: world\\nOrganization: University of North Carolina, Chapel Hill\\nLines: 354\\nSupersedes: <>\\nNNTP-Posting-Host: mahler.cs.unc.edu\\n\\nArchive-name: space/groups\\nLast-modified: $Date: 93/04/01 14:39:08 $\\n\\nSPACE ACTIVIST/INTEREST/RESEARCH GROUPS AND SPACE PUBLICATIONS\\n\\n    GROUPS\\n\\n    AIA -- Aerospace Industry Association. Professional group, with primary\\n\\tmembership of major aerospace firms. Headquartered in the DC area.\\n\\tActs as the \\\"voice of the aerospace industry\\\" -- and it's opinions\\n\\tare usually backed up by reams of analyses and the reputations of\\n\\tthe firms in AIA.\\n\\n\\t    [address needed]\\n\\n    AIAA -- American Institute of Aeronautics and Astronautics.\\n\\tProfessional association, with somewhere about 30,000-40,000\\n\\tmembers. 65 local chapters around the country -- largest chapters\\n\\tare DC area , LA , San Francisco , Seattle/NW , Houston  and Orange County\\n\\t, plus student chapters. Not a union, but acts to represent\\n\\taviation and space professionals  nationwide. Holds over 30 conferences a year on space and\\n\\taviation topics publishes technical Journals , technical reference books\\n\\tand is _THE_ source on current aerospace state of the art through\\n\\ttheir published papers and proceedings. Also offers continuing\\n\\teducation classes on aerospace design. Has over 60 technical\\n\\tcommittees, and over 30 committees for industry standards. AIAA acts\\n\\tas a professional society -- offers a centralized resume/jobs\\n\\tfunction, provides classes on job search, offers low-cost health and\\n\\tlife insurance, and lobbies for appropriate legislation . Very active public policy arm -- works\\n\\tdirectly with the media, congress and government agencies as a\\n\\tlegislative liaison and clearinghouse for inquiries about aerospace\\n\\ttechnology technical issues. Reasonably non-partisan, in that they\\n\\trepresent the industry as a whole, and not a single company,\\n\\torganization, or viewpoint.\\n\\n\\tMembership $70/yr .\\n\\n\\tAmerican Institute of Aeronautics and Astronautics\\n\\tThe Aerospace Center\\n\\t370 L'Enfant Promenade, SW\\n\\tWashington, DC 20077-0820\\n\\t-646-7400\\n\\n    AMSAT - develops small satellites  for a variety of\\n\\tuses by amateur radio enthusiasts. Has various publications,\\n\\tsupplies QuickTrak satellite tracking software for PC/Mac/Amiga etc.\\n\\n\\tAmateur Satellite Corporation \\n\\tP.O. Box 27\\n\\tWashington, DC 20044\\n\\t-589-6062\\n\\n    ASERA - Australian Space Engineering and Research Association. An\\n\\tAustralian non-profit organisation to coordinate, promote, and\\n\\tconduct space R&D projects in Australia, involving both Australian\\n\\tand international  collaborators. Activities\\n\\tinclude the development of sounding rockets, small satellites\\n\\t, high-altitude research balloons, and\\n\\tappropriate payloads. Provides student projects at all levels, and\\n\\tis open to any person or organisation interested in participating.\\n\\tPublishes a monthly newsletter and a quarterly technical journal.\\n\\n\\tMembership $A100 \\n\\tSubscriptions $A25  $A50 \\n\\n\\tASERA Ltd\\n\\tPO Box 184\\n\\tRyde, NSW, Australia, 2112\\n\\temail: \\n\\n    BIS - British Interplanetary Society. Probably the oldest pro-space\\n\\tgroup, BIS publishes two excellent journals: _Spaceflight_, covering\\n\\tcurrent space activities, and the _Journal of the BIS_, containing\\n\\ttechnical papers on space activities from near-term space probes to\\n\\tinterstellar missions. BIS has published a design study for an\\n\\tinterstellar probe called _Daedalus_.\\n\\n\\tBritish Interplanetary Society\\n\\t27/29 South Lambeth Road\\n\\tLondon SW8 1SZ\\n\\tENGLAND\\n\\n\\tNo dues information available at present.\\n\\n    ISU - International Space University. ISU is a non-profit international\\n\\tgraduate-level educational institution dedicated to promoting the\\n\\tpeaceful exploration and development of space through multi-cultural\\n\\tand multi-disciplinary space education and research. For further\\n\\tinformation on ISU's summer session program or Permanent Campus\\n\\tactivities please send messages to '' or\\n\\tcontact the ISU Executive Offices at:\\n\\n\\tInternational Space University\\n\\t955 Massachusetts Avenue 7th Floor\\n\\tCambridge, MA 02139\\n\\t-354-1987 \\n\\t-354-7666 \\n\\n    L-5 Society . Founded by Keith and Carolyn Henson in 1975 to\\n\\tadvocate space colonization. Its major success was in preventing US\\n\\tparticipation in the UN \\\"Moon Treaty\\\" in the late 1970s. Merged with\\n\\tthe National Space Institute in 1987, forming the National Space\\n\\tSociety.\\n\\n    NSC - National Space Club. Open for general membership, but not well\\n\\tknown at all. Primarily comprised of professionals in aerospace\\n\\tindustry. Acts as information conduit and social gathering group.\\n\\tActive in DC, with a chapter in LA. Monthly meetings with invited\\n\\tspeakers who are \\\"heavy hitters\\\" in the field. Annual \\\"Outlook on\\n\\tSpace\\\" conference is _the_ definitive source of data on government\\n\\tannual planning for space programs. Cheap membership .\\n\\n\\t    [address needed]\\n\\n    NSS - the National Space Society. NSS\",\n          \" Re: arcade style buttons and joysticks\\nOrganization: Antone's Italian Kitchen and Excellence in Operating Network\\nX-Newsreader: rusnews v1.02\\nLines: 26\\n\\n  writes:\\n\\n> Hi there,\\n> Can anyone tell me where it is possible to purchase controls found\\n> on most arcade style games.  Many projects I am working on would\\n> be greatly augmented if I could implement them.  Thanx in advance.\\n> \\n> -Dave\\n> \\n> \\n\\nContact Chris Arthur at \\nHe restores lots of old video and arcade games and knows where to get\\nparts.\\n\\nTony\\n\\n-----------------------------------------------------------------------\\n-- Anthony S. Pelliccio, kd1nr/ae    // Yes, you read it right, the  //\\n-- system @ garlic.sbs.com          // man who went from No-Code    //\\n-----------------------------------//  to Extra in     //\\n-- Flame Retardent Sysadmin       // exactly one year!            //\\n-------------------------------------------------------------------\\n-- This is a calm .sig! --\\n--------------------------\\n\\n\",\n          \"Re: Modified sense of taste in Cancer pt?\\nOrganization: University of Northern Iowa\\nLines: 16\\n\\nIn article <>,   writes:\\n> \\n> What does a lack of taste of foods, or a sense of taste that seems \\\"off\\\"\\n> when eating foods in someone who has cancer mean? What are the possible\\n> causes of this? Why does it happen?\\n\\nI can't answer most of your questions, but I've seen it happen in \\nfamily members who are being treated with radiation and/or chemotherapy.\\nJory Graham published a cookbook many years ago  called \\\"Something has to taste\\ngood\\\" .\\n\\nThe cookbook was just what we needed several times when favorite foods\\nsuddenly became \\\"yech\\\".\\n\\nKay Klier  Biology Dept  UNI\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 14,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          14,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embeddings\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outlier\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"True\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_outliers"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-cb131950-93cc-4d3d-b158-c8054f9a88e2\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Label</th>\n",
              "      <th>Class Name</th>\n",
              "      <th>Embeddings</th>\n",
              "      <th>Outlier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Re: The [secret] source of that announcement\\...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.027169958, 0.0180854, -0.079990745, 0.01548...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Re: White House Wiretap Chip Disinformation S...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.011647083, 0.006911768, -0.028479766, 0.034...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Re: Why the clipper algorithm is secret\\nOrga...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.021074744, 0.08058417, 0.0095778825, 0.0386...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Re: Once tapped, your code is no good any mor...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.03491447, 0.023669688, -0.049023017, 0.0275...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Graham Toal &lt;&gt;Re: The battle is joined\\nOrigin...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.05105832, -0.009382685, -0.035797328, 0.016...</td>\n",
              "      <td>True</td>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                Text  Label Class Name  \\\n",
              "1   Re: The [secret] source of that announcement\\...     11  sci.crypt   \n",
              "3   Re: White House Wiretap Chip Disinformation S...     11  sci.crypt   \n",
              "5   Re: Why the clipper algorithm is secret\\nOrga...     11  sci.crypt   \n",
              "6   Re: Once tapped, your code is no good any mor...     11  sci.crypt   \n",
              "9  Graham Toal <>Re: The battle is joined\\nOrigin...     11  sci.crypt   \n",
              "\n",
              "                                          Embeddings Outlier  \n",
              "1  [0.027169958, 0.0180854, -0.079990745, 0.01548...    True  \n",
              "3  [0.011647083, 0.006911768, -0.028479766, 0.034...    True  \n",
              "5  [0.021074744, 0.08058417, 0.0095778825, 0.0386...    True  \n",
              "6  [0.03491447, 0.023669688, -0.049023017, 0.0275...    True  \n",
              "9  [0.05105832, -0.009382685, -0.035797328, 0.016...    True  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the points that are outliers\n",
        "RADIUS = 0.62\n",
        "detect_outlier(df_train, emb_c, RADIUS)\n",
        "df_outliers = df_train[df_train[\"Outlier\"] == True]\n",
        "df_outliers.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "h_wbM5yYE4MS"
      },
      "outputs": [],
      "source": [
        "# Use the index to map the outlier points back to the projected TSNE points\n",
        "outliers_projected = df_tsne.loc[df_outliers[\"Outlier\"].index]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCt4wfYdoTJz"
      },
      "source": [
        "Plot the outliers and denote them using a transparent red color."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "IrAKwBp0TaNu"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 6))  # Set figsize\n",
        "plt.rcParams.update({\"font.size\": 10})\n",
        "sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x=\"TSNE1\", y=\"TSNE2\", hue=\"Class Name\", palette=\"Set2\")\n",
        "sns.scatterplot(\n",
        "    data=centroids,\n",
        "    x=\"TSNE1\",\n",
        "    y=\"TSNE2\",\n",
        "    color=\"black\",\n",
        "    marker=\"X\",\n",
        "    s=100,\n",
        "    label=\"Centroids\",\n",
        ")\n",
        "# Draw a red circle around the outliers\n",
        "sns.scatterplot(\n",
        "    data=outliers_projected,\n",
        "    x=\"TSNE1\",\n",
        "    y=\"TSNE2\",\n",
        "    color=\"red\",\n",
        "    marker=\"o\",\n",
        "    alpha=0.5,\n",
        "    s=90,\n",
        "    label=\"Outliers\",\n",
        ")\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title(\"Scatter plot of news with outliers projected with t-SNE\")\n",
        "plt.xlabel(\"TSNE1\")\n",
        "plt.ylabel(\"TSNE2\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVm_A9HmGwEN"
      },
      "source": [
        "Use the index values of the datafames to print a few examples of what outliers can look like in each category. Here, the first data point from each category is printed out. Explore other points in each category to see data that are deemed as outliers, or anomalies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "lpZ-hcDvG13M"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: The [secret] source of that announcement\n",
            "Organization: DSI/USCRPAC\n",
            "Lines: 23\n",
            "\n",
            "\n",
            " suggests using a common but restricted-distribution private\n",
            "key to allow public key system encrypted postings. In theory that will work\n",
            "fine as long as the privae key remains secure.\n",
            "\n",
            "In practice it would be a good idea to check to see if that would be a\n",
            "violation of some net rule, practice, custom, etc. I don't say it would be,\n",
            "just that it would be a good idea to check. This is not like rot13 where\n",
            "everybody can have the key trivially.\n",
            "\n",
            "It would also be a good idea to check to see if such posts would be\n",
            "forwarded by the sites needed to make the chain work.\n",
            "\n",
            "Of course there'd be no problem with a discussion group travelling over\n",
            "facilities entirely under the control of the members. Probably there would\n",
            "also be no problem with a mailing list approach. It might even  be fun for\n",
            "some.\n",
            "\n",
            "-- \n",
            "David Sternlight         Great care has been taken to ensure the accuracy of\n",
            "                         our information, errors and omissions excepted.  \n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_crypt_outliers = df_outliers[df_outliers[\"Class Name\"] == \"sci.crypt\"]\n",
        "print(sci_crypt_outliers[\"Text\"].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "SPsQB3eHJN25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: arcade style buttons and joysticks\n",
            "Organization: Antone's Italian Kitchen and Excellence in Operating Network\n",
            "X-Newsreader: rusnews v1.02\n",
            "Lines: 26\n",
            "\n",
            "  writes:\n",
            "\n",
            "> Hi there,\n",
            "> Can anyone tell me where it is possible to purchase controls found\n",
            "> on most arcade style games.  Many projects I am working on would\n",
            "> be greatly augmented if I could implement them.  Thanx in advance.\n",
            "> \n",
            "> -Dave\n",
            "> \n",
            "> \n",
            "\n",
            "Contact Chris Arthur at \n",
            "He restores lots of old video and arcade games and knows where to get\n",
            "parts.\n",
            "\n",
            "Tony\n",
            "\n",
            "-----------------------------------------------------------------------\n",
            "-- Anthony S. Pelliccio, kd1nr/ae    // Yes, you read it right, the  //\n",
            "-- system @ garlic.sbs.com          // man who went from No-Code    //\n",
            "-----------------------------------//  to Extra in     //\n",
            "-- Flame Retardent Sysadmin       // exactly one year!            //\n",
            "-------------------------------------------------------------------\n",
            "-- This is a calm .sig! --\n",
            "--------------------------\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_elec_outliers = df_outliers[df_outliers[\"Class Name\"] == \"sci.electronics\"]\n",
        "print(sci_elec_outliers[\"Text\"].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "APPg8TURJ9yt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: Is MSG sensitivity superstition?\n",
            "Organization: your service\n",
            "Lines: 20\n",
            "NNTP-Posting-Host: hpctdkz.col.hp.com\n",
            "\n",
            "\n",
            "Jason Chen writes:\n",
            "> Now here is a new one: vomiting. My guess is that MSG becomes the number one\n",
            "> suspect of any problem. In this case. it might be just food poisoning. But\n",
            "> if you heard things about MSG, you may think it must be it.\n",
            "\n",
            "----------\n",
            "\n",
            "Yeah, it might, if you only read the part you quoted.  You somehow left \n",
            "out the part about \"we all ate the same thing.\"  Changes things a bit, eh?\n",
            "\n",
            "You complain that people blame MSG automatically, since it's an unknown and\n",
            "therefore must be the cause.  It is equally  unreasonable to\n",
            "defend it, automatically assuming that it CAN'T be the culprit.\n",
            "\n",
            "Pepper makes me sneeze.  If it doesn't affect you the same way, fine.\n",
            "Just don't tell me I'm wrong for saying so.\n",
            "\n",
            "These people aren't condemning Chinese food, Mr. Chen - just one of its \n",
            " ingredients.  Try not to take it so personally.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_med_outliers = df_outliers[df_outliers[\"Class Name\"] == \"sci.med\"]\n",
        "print(sci_med_outliers[\"Text\"].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "WeoJF7c8KB49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: Abyss--breathing fluids\n",
            "Organization: U.C. Berkeley Math. Department.\n",
            "Lines: 19\n",
            "NNTP-Posting-Host: skippy.berkeley.edu\n",
            "\n",
            "Are breathable liquids possible?\n",
            "\n",
            "I remember seeing an old Nova or The Nature of Things where this idea was\n",
            "touched upon .  If nothing else, I know\n",
            "such liquids ARE possible because...\n",
            "\n",
            "They showed a large glass full of this liquid, and put a white mouse  in\n",
            "it.  Since the liquid was not dense, the mouse would float, so it was held down\n",
            "by tongs clutching its tail.  The thing struggled quite a bit, but it was\n",
            "certainly held down long enough so that it was breathing the liquid.  It never\n",
            "did slow down in its frantic attempts to swim to the top.\n",
            "\n",
            "Now, this may not have been the most humane of demonstrations, but it certainly\n",
            "shows breathable liquids can be made.\n",
            "-- \n",
            "*Isaac Kuo \t*       ___\n",
            "*\t\t\t\t\t* _____/_o_\\_____\n",
            "*\tTwinkle, twinkle, little .sig,\t*(====)\n",
            "*\tKeep it less than 5 lines big.\t* \\==\\/     \\/==/\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_space_outliers = df_outliers[df_outliers[\"Class Name\"] == \"sci.space\"]\n",
        "print(sci_space_outliers[\"Text\"].iloc[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siaPlEJhh0pr"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that t-SNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis)."
      ]
    }
  ],
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    "colab": {
      "name": "Anomaly_detection_with_embeddings.ipynb",
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